Which use cases would benefit most from continuous event stream processing? (Choose three.)
A.
Fraud detection
B.
Context-aware product recommendations for e-commerce
C.
End-of-day financial settlement processing
D.
Log monitoring/application fault detection
E.
Historical dashboards
The Answer Is:
A, B, D
This question includes an explanation.
Explanation:
Event stream processing is ideal for real-time and near-real-time scenarios. Kafka Streams and ksqlDB are commonly used in use cases requiring immediate reaction to data.
A → Correct: Fraud detection systems benefit from low-latency anomaly detection.
B → Correct: Real-time product recommendations based on user activity require continuous processing.
C → Incorrect: End-of-day processing is batch-oriented, not real-time.
D → Correct: Real-time alerting and log anomaly detection are classic streaming use cases.
E → Incorrect: Historical dashboards rely on batch aggregation and are not continuous processing.
Page Reference:
Kafka: The Definitive Guide, 1st Edition, Chapter 7, p. 210–215
Confluent Documentation: Use Cases for Kafka Streams and ksqlDB
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